| | --- |
| | license: other |
| | tags: |
| | - science |
| | - mathematics |
| | - reasoning |
| | - multiple-choice |
| | - question-answering |
| | task_categories: |
| | - multiple-choice |
| | - math |
| | - question-answering |
| | language: |
| | - en |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | # StreetMath Dataset |
| |
|
| | ## Dataset Summary |
| |
|
| | The **Street Math dataset** is a synthetic reasoning benchmark that evaluates a model’s ability to **approximate sums of decimal prices** in everyday shopping scenarios. |
| | Each example presents a list of item prices, and the model must select the approximate total cost (before tax) from multiple-choice options. |
| |
|
| | The dataset is designed to test **numerical reasoning, estimation, and handling of decimal numbers**. |
| | Language: **English**. |
| | Domain: **mathematics applied to real-world shopping tasks**. |
| |
|
| | ## Languages |
| |
|
| | - **English (en)**: prompts and options are written in plain English, with U.S. dollar formatting for prices. |
| |
|
| | ## Data Instances |
| |
|
| | Example instance: |
| |
|
| | ```json |
| | { |
| | "id": "basket_sum_000243", |
| | "topic": "basket_sum", |
| | "subtopic": "decimal_prices", |
| | "prompt": "You’re buying these items: $3.55, $15.42, $4.56, $12.63, $6.08. About how much will you pay (before tax)?", |
| | "labels": ["A", "B", "C", "D"], |
| | "correct_label": "A", |
| | "choices": ["$43.00", "$14.11", "$42.24", "$182.80"], |
| | "correct_option": 0, |
| | "metadata": { |
| | "exact_value": 42.24, |
| | "good_value": 43.0, |
| | "mild_value": 14.11, |
| | "way_value": 182.8, |
| | "prices": [3.55, 15.42, 4.56, 12.63, 6.08] |
| | }, |
| | "split": "test" |
| | } |
| | ``` |
| |
|
| | ## Intended Uses |
| |
|
| | The Basket Sum dataset is intended for: |
| | - **Benchmarking language models** on basic numerical reasoning and arithmetic in natural language contexts. |
| | - **Evaluating estimation skills**: testing whether models can provide approximate answers rather than exact calculations. |
| | - **Educational and research purposes**: studying how models handle everyday math tasks such as adding decimal prices. |
| |
|
| | This dataset is **not** intended for: |
| | - Financial or accounting applications. |
| | - Real-world shopping or economic forecasting. |
| | - Any critical decision-making where incorrect numerical outputs could cause harm. |
| |
|
| | ## Format |
| |
|
| | - **File type:** JSON Lines (`.jsonl`) |
| | - **Each line:** one example as a JSON object |
| | - **Compatible with:** Hugging Face `datasets` library (`load_dataset("json", data_files="...")`) |
| |
|
| |
|
| | ## How to Get the Dataset |
| |
|
| | You can easily load this dataset from the Hugging Face Hub using the `datasets` library: |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | # Load the dataset |
| | dataset = load_dataset("Chiung-Yi/StreetMath") |
| | |
| | # Access the test split |
| | test_dataset = dataset["test"] |
| | |
| | # Example: print the first item |
| | print(test_dataset[0]) |
| | ``` |
| |
|
| |
|
| | ## Limitations and Ethical Considerations |
| |
|
| | **Licensing**: The license is currently unspecified. For any public or commercial use, it is necessary to verify the terms with the author. |
| |
|
| |
|
| | ## Dataset Curators |
| |
|
| | - Original dataset created by [Chiung-Yi](https://huggingface.co/Chiung-Yi) |
| |
|
| | ### Disclaimer |
| | This dataset card was written by a community contributor to improve documentation. |
| | If you are the original author or know additional details, feel free to submit a pull request or open an issue to update this card. |
| |
|
